In recent years, research has been made about object detection to detect various types of objects (such as a person's face and a car) from an image that is captured by a camera or the like. The object detection technology includes learning the features of objects to be detected to create learning data in advance, and comparing the created learning data and image data to determine whether the objects to be detected are included in the image.
The image data itself contains an enormous amount of information while the object detection technology has only to determine whether there is an object to be searched for in the image. The image data therefore needs to be reduced to save memory resources by utilizing information quantization techniques.
There are information quantization techniques in which the image data is subjected to frequency conversion (wavelet transform), and quantization processing is performed based on the magnitudes of the resulting conversion coefficients (or the magnitudes of differences in pixel value between adjoining pixels) (for example, see H. Schneiderman and T. Kanade, “Object Detection Using the Statistics of Parts”, International Journal of Computer Vision, 2002, which is referred to as “Schneiderman” hereinafter). According to the quantization processing, the conversion coefficients and statically-set quantization thresholds are compared to quantize the image data in three levels. This can reduce the area for storing the image data and learning data intended for object detection.
In the technical field of image compression, there has been known a technology in which quantization steps for respective frequency components of the image are changed on the basis of differences (distortion) in pixel value between before and after image compression, thereby preventing the degradation of the image that is restored after quantization (for example, see Japanese Laid-open Patent Publication No. 2001-298366).
In the foregoing conventional techniques, the quantization processing is performed by comparing the conversion coefficients and statically-set quantization thresholds. It is therefore difficult to cope with the variety of images to be processed, and there has been the problem of a drop in the accuracy of object detection.
More specifically, the images to be subjected to object detection include various types of images such as bright, dim, and backlit ones. To perform accurate object detection on any type of image, it is desired to quantize the images while preserving the subjects' features necessary for object detection.
Like the known technology, there is a technique to avoid image degradation by changing quantization steps for the respective frequency components of an image on the basis of differences in pixel value between before and after image compression. Such a technique is not applicable to the object detection since the object detection involves no image compression.